This paper is accepted for AMA Summer Educators’ Conference 2002 in San Diego
and will be published in the conference proceedings
EXPLORING THE SERVICE RECOVERY PARADOX*
Stefan Michel
Hochschule fuer Wirtschaft Lucerne, Switzerland
Service recovery paradox refers to situations where satisfaction of recovered customers actually exceeds that of customers who have not encountered any problems with the initial service. The growing debate on the existence of a service recovery paradox has not yet reached a conclusion. Based on
interviews with more than 11,000 customers of a bank, our results lead to the conclusion that a very
satisfying initial service is what is most preferred. Nevertheless, a very good service recovery leads to
higher overall satisfaction and more positive word-of-mouth intention than an error-free initial service which is “just” satisfying.
* The study is supported jointly by a Swiss Bank, by the Commission for Innovation and Technology (KTI) of the Swiss government and by the Hochschule fuer Wirtschaft Lucerne.
EXPLORING THE SERVICE RECOVERY PARADOX
ABSTRACT
Service recovery paradox refers to situations where satisfaction of recovered customers actually
exceeds that of customers who have not encountered any problems with the initial service. The
growing debate on the existence of a service recovery paradox has not yet reached a conclusion.
Based on interviews with more than 11,000 customers of a bank, our results lead to the conclusion
that a very satisfying initial service is what is most preferred. Nevertheless, a very good service
recovery leads to higher overall satisfaction and more positive word-of-mouth intention than an
error-free initial service which is “just” satisfying.
SERVICE RECOVERY PARADOX
Service recovery has gained increasing attention in services marketing literature in recent years (e.g.
Tax et al. 1998; Smith et al. 1999; McCollough et al. 2000; Andreassen 2001; Swanson and Kelley
2001). Since service failure leads to negative disconfirmation and to dissatisfaction, it is suggested
that appropriate service recovery can restore a dissatisfied customer to a state of satisfaction. A early
definition of service recovery was suggested by Grönroos (1988): “Service recovery refers to the
actions a service provider takes in response to service failure.” More recently, Smith et al. (1999)
treat “service recovery as a ‘bundle of resources’ that an organization can employ in response to a
failure.” Service recovery differs from complaint management in its focus on service failures and the
company’s immediate reaction to it. Complaint management is based on customer complaints that
may be triggered by service failures. However, since most dissatisfied customers are reluctant to
complain (Andreasen and Best 1977; Singh 1990), service recovery attempts to solve problems at
the service encounter before customers complain or before they leave the service encounter dissatisfied (Lewis 1996). Both complaint management and service recovery are considered to be customer
retention strategies (Halstead et al. 1996).
The term “recovery paradox” (McCollough and Bharadwaj 1992) refers to situations where
satisfaction and repurchase rates of recovered customers actually exceed those of customers who
have not encountered any problems (Blanchard 1993; Oliver 1996).
LITERATURE REVIEW
The growing debate on the existence of a service recovery paradox has not yet reached a conclusion.
In one of the earliest studies concerning service recovery, Berry et al. (1990) found that service quality is highest when no failure happened. If a failure is resolved satisfactorily, about half of the loss on
the satisfaction score can be recovered. Remarkably, this is one of the rare studies in the field that
used actual customers (not students) as respondents, who evaluated a real-life service encounter (as
opposed to a written scenario) where dissatisfied customers and a control group were compared (not
only complainers and non-complainers). The existence of a service recovery paradox was further
denied (by Halstead and Page 1992; Brown et al. 1996; Boshoff 1997; McCollough et al. 2000, and
Andreassen 2001) in various settings. Most of these authors conclude that there is no way to please
customers more than with a reliable, first-time error-free service. According to these findings, service
recovery is considered a strategy to limit the harm caused by a service failure rather than to impress
the customer with a special effort when something goes wrong. Nevertheless, a positive impact of
service recovery on dependent variables such as satisfaction, image, and loyalty is still suggested.
2
Based on almost 700 critical incidents from the airline, hotel and restaurant industries, Bitner et al.
(1990) concluded that failures could be remembered as highly satisfactory encounters if they were
handled properly.
In summary, results of empirical studies on service recovery paradox are not only contradictory but
also difficult to compare. Discrepancies result from differences in (a) research methodology, (b) the
incidents in question, and (c) the dependent variable.
(a) Using scenario-based experiments is common in studying service recovery (Boshoff 1997; Brown
et al. 1996; Dubé and Maute 1996; Kelley and Davis 1994; McCollough et al. 2000; Smith and Bolton 1998; Smith et al. 1999; Swanson and Kelley 2001; Webster and Sundaram 1998). Although
external validity is claimed for this methodology, the service recovery paradox effect may be underestimated. Experimental situations are more cognitively controlled, and the respondents are not involved emotionally as they would be in real-life settings. Assuming that emotions do play an important role in recovery situations (Dubé and Maute 1995), respondents may conclude logically that a
good service in the first place has to be better than a recovered failure. Because of this logic, the
term “paradox” has been coined.
(b) The service recovery process starts with customer dissatisfaction and not with a customer complaint. Complaint handling is only one aspect of service recovery, since most dissatisfied customers
do not complain (Andreasen and Best 1977; Singh 1990). If complainers are compared to noncomplainers, service recovery paradox is more likely to be found. It can be assumed that dissatisfied
customers in the non-complainer group diminish overall satisfaction and loyalty, whereas complainers are often highly involved and may reward service recovery particularly well. Therefore, very successful complaint handling may exceed the increase in the level of satisfaction and loyalty above average. Consequently, studies on service recovery paradox should not be based on complaining customers, but on dissatisfied customers who must be compared to satisfied customers.
(c) Several suggestions for the dependent variable of the service recovery paradox have been put
forward in the literature. Some researchers argue that service recovery will impact satisfaction
(Andreassen 2000; Boshoff 1997; McCollough et al. 2000; Smith et al. 1999), service quality (Berry
et al. 1990), loyalty (Chung and Hoffman 1998), repurchase intention (Halstead and Page 1992),
positive word-of-mouth behavior (Swanson and Kelley 2001), image (Andreassen 2001), commitment and trust (Bejou and Palmer 1998; Tax et al. 1998), and combinations of those constructs
(Dubé and Maute 1996; Smith and Bolton 1998; Spreng et al. 1995; Webster and Sundaram 1998).
Moreover, satisfaction as well as loyalty is not defined uniformly across the studies. Regarding satisfaction, either transaction-specific satisfaction or overall satisfaction is concerned. Loyalty, on the
other hand, is measured as intended loyalty, word-of-mouth behavior, or repurchase behavior.
3
The following overview provides a comprehensive picture of the proposition that service recovery
studies hold more dissimilarities than similarities.
SATISFACTION
Satisfaction as a construct is commonly defined by the expectancy disconfirmation paradigm (Oliver
1980), by equity theory (Oliver and Swan 1989a), or by attribution theory (Weiner 1986). However,
it is justified to focus on the expectancy disconfirmation paradigm, since it dominates the literature
and is able to integrate the other two approaches (Stauss 1999).
In the simple model suggested by Oliver (1996), satisfaction results from expectations and from disconfirmation. If expectations are met or exceeded, positive disconfirmation leads to satisfaction. On
the other hand, if expectations are not met, negative disconfirmation leads to dissatisfaction.
In service recovery research, there is not just one but at least two evaluation phases. As Oliver
(1996) puts it: “Bear in mind, however, that recovery is necessitated by dissatisfaction”. Service recovery starts, by definition, with initial customer dissatisfaction. After this first evaluation, customers
go through a recovery process leading to a second evaluation. In a study with 410 complaining customers of an interstate moving company, Spreng et al. (1995) found that satisfaction with recovery
has more impact on intention and word-of-mouth than satisfaction with the initial service.
Parasuraman et al. (1991) argue that service-failure situations tend to raise customers’ adequate service level temporarily, thereby narrowing the zone of tolerance. The service recovery process is important because of lower expectations and higher zone of tolerance. Additionally, a study by
Andreassen (2000) revealed that disconfirmation rather than expectations have a dominant impact on
satisfaction.
The impact of service recovery performance on satisfaction is assessed on two different levels. Service recovery affects encounter satisfaction (McCollough et al. 2000; Smith et al. 1998) as well as
overall satisfaction with the company (Brown et al. 1996).
While the impact of recovery performance on encounter satisfaction is well documented in the literature, (Brown et al. 1996) are one of the few, if not the only, researchers who tested for the link between recovery performance and overall satisfaction. Their findings suggest that service recovery is
important for encounter satisfaction but does not improve long-term-oriented measures. This is contrary to the assumption that overall satisfaction is based on an accumulation of encounter-specific
evaluations (DeSarbo and Oliver 1988). According to the concept of “zone of tolerance” (Johnston
1995b; Zeithaml et al. 1993), recovery performance that exceeds desired expectations (Parasuraman
et al. 1991) develops customer franchise. (Johnston 1995b) suggests that incursion above the zone of
tolerance (e.g., state of delight) will result in high satisfaction, while performances within the zone of
tolerance may not be noticed. This suggests that a service recovery paradox is likely to occur for
4
excellent recoveries compared to mediocre error-free service transactions, but not compared to excellent error-free service transactions.
Therefore, we hypothesize
H1a: Customers who experienced a service failure followed by a recovery which was much better
than expected do not show higher overall satisfaction rates than customers who report an error-free
and very satisfying transaction in the first place.
H1b: Customers who experienced a service failure followed by a recovery which was much better
than expected show higher overall satisfaction rates than customers who report an error-free and
just satisfying transaction in the first place.
WORD-OF-MOUTH
Not only will positive disconfirmation with service recovery increase overall satisfaction but also
positive word-of-mouth (Spreng et al. 1995; Swanson and Kelley 2001). For example, a study by
(Oliver and Swan 1989b) shows strong links from satisfaction with complaint handling to word-ofmouth behavior. That means that customers who were recovered successfully recommend the company to others. This is important since many services possess credence qualities (Zeithaml and Bitner
1996), and since word-of-mouth communication can have an extremely powerful influence on the
consumer purchasing process (Richins 1983). In correspondence to the proposed effect on overall
satisfaction, we hypothesize that
H2a: Customers who experienced a service failure followed by a recovery which was much better
than expected show higher recommendation intentions than customers who report an error-free and
very satisfying transaction in the first place.
H2b: Customers who experienced a service failure followed by a recovery which was much better
than expected show higher recommendation intentions than customers who report an error-free and
just satisfying transaction in the first place.
PERCEIVED JUSTICE
Recent contributions have shown that perceived justice is a significant construct for evaluating service recovery (Smith et al. 1999; Tax et al. 1998). When perceived justice is high, customers feel
fairly treated by the service company. Whenever customers report a service failure, it must be assumed that this failure is, to some extent, an “unfair” treatment of the customer. Hence, service recovery must reestablish a situation of fairness in the perception of the customer.
Clemmer and Schneider (1996) revealed that the justice concept consists of three dimensions: distributive justice, procedural justice, and interactional justice. Distributive justice focuses on the allo5
cation of benefits and costs (Deutsch 1985). Customers consider the benefit they received from a
service in the light of the cost associated with the service (money, time, etc.). Procedural justice does
not focus on the results of a transaction, but rather on the process by which the results are obtained
(Lind and Tyler 1988). Finally, interactional justice considers interpersonal fairness. Since our study
was based neither on scenarios nor on written complaint letters, the variety of justice aspects was
very broad. As a consequence, only very general items could be used in the survey. For distributive
justice, we asked whether the service recovery solution was satisfactory. Procedural justice was
measured by response speed (Smith et al. 1999), whereas interactional justice was dropped because
many service failures and recoveries were not based on personal interaction.
According to economic principles and everyday experiences, customers who received greater benefit
to lesser costs are more satisfied. Hence, distributive justice will enhance positive disconfirmation
with service recovery.
But the outcome of a service recovery is not the only thing that matters. Since customers are present
at most service encounters, timing is also important. Customers do not want to wait for a service,
they prefer a quick response to their request (Bitner et al. 1990; Parasuraman et al. 1985; Taylor
1994). This is especially true for situations in which something has gone wrong. Accordingly, complaint literature has identified speed as an important factor of procedural justice (Clemmer and
Schneider 1996; Tax et al. 1998). Since speed is an aspect of procedural justice, and since justice is
supposed to influence customer satisfaction positively, we hypothesize that a speedy recovery will
enhance positive disconfirmation with service recovery. Hence,
H3a: Distributive justice (solution provided) and procedural justice (speed of recovery) have a positive impact on disconfirmation with service recovery.
Satisfaction research indicates that not all satisfaction factors possess a linear impact on dependent
measures such as overall satisfaction, quality and loyalty. (Oliver 1996) distinguishes between three
categories. Monovalent satisfiers have a positive impact when expectations are met, while monovalent dissatisfiers have a negative impact when expectations are not met. Bivalent satisfiers are the
only factors that may lead to both a negative outcome (when expectations are not met) as well as to
a positive outcome (when expectations are met). In similar vein, a study by Cadotte and Turgeon
(1988) reveals four groups of factors: dissatisfiers, satisfiers, criticals and neutrals.
Within the context of a service failure, a speedy recovery that leads to a fair solution is a “must have”
rather than a “should have” or “nice to have” attribute. Accordingly, distributive justice and procedural justice are assumed to be monovalent dissatisfiers.
H3b: Impact of distributive justice (solution provided) and procedural justice (speed of recovery) on
recovery disconfirmation is not linear. Both have a stronger impact on negative than on positive disconfirmation with service recovery.
6
METHODOLOGY
Despite growing interest in service recovery, methodological problems regarding the measurement of
service recovery antecedents, processes and outcomes remain evident. Most empirical work has been
done either by collecting actual critical incidents from respondents (Chung and Hoffman 1998;
Edvardsson and Strandvik 1999; Johnston 1995a; Kelley et al. 1993), by describing hypothetical
scenarios to respondents (Dubé and Maute 1996; Kelley and Davis 1994; Smith et al. 1998), or by
using written complaints (Tax et al. 1998).
However, “very little research has directly compared post-recovery satisfaction with consumers who
experienced an error-free service…” (McCollough et al. 2000).
Our study overcomes this limitation by using a stratified random sampling of satisfied and dissatisfied
customers instead of focusing on dissatisfied or complaining customers only. The survey used is
process-oriented rather than dimension-oriented like SERVQUAL. Similar approaches have been
suggested by (Botschen et al. 1996; Michel 2001; Stauss and Weinlich 1997). Botschen et al. (1996)
introduced the SOPI (sequence-oriented problem identification) research method that includes: (1)
blueprinting the sequence of steps that occur in a service encounter, (2) asking customers to provide
evaluations for each step they experience in the process of interacting with a service provider or a
service-related process, and (3) assessing their responses. Since our main focus is service recovery,
the respondents did not evaluate each step in the process. In this respect, we did not follow the SOPI
procedure. But we did use blueprints to track the path of the customer’s service experience before
we asked for specific negative incidents.
The disadvantage of this design for the purpose of studying service recovery lies in its costs. It is
difficult to design a survey that encompasses complex blueprints for various processes. And it is
expensive to go through all process steps with all respondents. Finally, only a minority will report a
service failure, making a multiple over-sampling necessary. As a consequence, this kind of design is
efficient only as part of a general service quality and customer satisfaction study, as was the case in
our setting.
STUDY
Our study was conducted within the framework of a large-scale service-quality study of a major
Swiss bank. After identifying six service core processes, the sampling procedure randomly selected
customers of different branches who were involved in at least one of the six processes. 11,929
customers participated in interviews of an average length of 15 minutes. 61.5% of the respondents
were male, 38.5% were female. This quota mirrors the distribution of the primary contact person
within the customers’ households. 12.3% of the respondents were younger than 30, 24.8% were
7
between 30 and 39, 21.2% were between 40 and 49, 20.6% were between 50 and 59, and 21.1% of
the respondents were over 60.
To overcome possible limitations due to recall and time-lag effects, the respective transaction must
have taken place no more than eight weeks before the interview. After evaluating overall satisfaction
with the bank and its representatives, every step of the service process was checked by means of a
questionnaire based on the blueprints for each core process. The many variants of these blueprints
led to more than 450 items that could only be handled by interactive interviewing systems.
Whenever a service failure was reported, the interviewer switched to a specific service recovery
questionnaire. Since the context of the recovery incident was already known, the customer’s
perception of the type of failure, and its frequency, magnitude, cause, handling and outcome were
assessed. By using this step-by-step questionnaire, the impact of service failures and service
recoveries on overall process and outcome satisfaction could be measured against the control group
who did not report a service failure.
Customers who reported a failure or error in the core process or in another process during the last
twelve months were then asked to specify the failure incident. The following standardized questions
were used for this part of the questionnaire:
•
Please describe the failure or error in detail. What exactly happened?
•
How did the staff react to the incident?
•
What reaction did you expect from the staff?
For the present study, failure incidents were only considered if the verbal description was precise
enough to comprehend what the failure was. Furthermore, if customers were dissatisfied with bank
policies (e.g., interest rates), if they attributed the failure to a third party, or if the service recovery
process was still pending, the incident was omitted from further analysis. Satisfaction with recovery
speed and recovery solution, disconfirmation with service recovery, overall satisfaction, and intention
to recommend were measured by using a 5-point likert scale.
RESULTS
The analyzed sample was split into two main groups with five subgroups each. The first five groups
in table 1 are customers who reported a service failure (n=1078). The control group (6 to 10) is defined as customers who did not report a failure (n=8243). The service recovery group was further
split according to disconfirmation with service recovery, while the control group was split according
to satisfaction with initial (error-free) service.
Table 1 shows the impact of service recovery on the dependent variables “overall satisfaction” and
“recommendation intention” for all of the 10 subgroups.
8
Table 1
Impact of service recovery and transaction satisfaction on overall satisfaction and
recommendation intention
Means for customer groups
Customer group
1 Service recovery
much better than
expected
2 Service recovery
better than expected
3 Service recovery as
expected
4 Service recovery
worse than expected
5 Service recovery
much worse than
expected
6 w/o failure, very
satisfied with process
7 w/o failure, satisfied
with process
8 w/o failure, neither
satisfied nor
dissatisfied with
process
9 w/o failure,
dissatisfied with
process
10 w/o failure
dissatisfied with
process
Total
Mean
N
Std. Deviation
Mean
N
Std. Deviation
Mean
N
Std. Deviation
Mean
N
Std. Deviation
Mean
N
Std. Deviation
Mean
N
Std. Deviation
Mean
N
Std. Deviation
Mean
N
Std. Deviation
Mean
N
Std. Deviation
Mean
N
Std. Deviation
Mean
N
Std. Deviation
Satisfaction with bank
(1=very satisfied)
1.72
50
.573
1.99
105
.753
2.04
674
Recommend the bank
(1=very likely)
1.52
50
.735
1.90
102
1.000
2.03
659
.770
1.011
2.31
176
.880
2.81
53
1.093
1.45
4562
.575
1.88
3491
.558
2.16
279
.704
2.24
90
.798
2.42
36
1.079
1.71
9516
.667
2.26
171
.923
2.98
53
1.337
1.46
4489
.738
1.72
3408
.817
2.10
269
.989
2.22
86
1.162
2.38
34
1.371
1.66
9321
.849
In order to test for service recovery paradox, mean differences of four groups are analyzed. Customers within group 1 and 2 perceived service recovery as “much better than expected” or “better than
expected” respectively. Customers within group 6 and 7 did not encounter a service failure and were
“very satisfied” or “satisfied” with the initial transaction. Figures 1 and 2 show the means for overall
satisfaction and recommendation intention respectively.
9
Figure 1 Mean scores for overall satisfaction
Figure 2 Mean scores for recommenda-
1 SR much better
1 SR much better
2 SR better than exp
2 SR better
6 w/o very satisfied
Customer group
Customer group
tion
7 w/o satisfied
1.4
1.5
1.6
1.7
1.8
1.9
2.0
6 w/o very satisfied
7 w/o satisfied
2.1
Satisfaction with bank (1=very satisfied)
1.4
1.5
1.6
1.7
1.8
1.9
2.0
Recommend the bank (1=very likely)
Since the Levene test showed that homogeneity of variances cannot be assumed, and since sample
sizes are very unequal, ANOVA and t-test are not appropriate. Instead, the Mann-Whitney U test
was applied for significance testing. Mean differences in overall satisfaction with the bank (figure 1)
are significant between group 1 and 6 (z=-3.587, p<0.01), 1 and 7 (z=-2.090, p<0.02) and 2 and 6
(z=-8.154, p<0.01). Customers who did not report a failure and who were very satisfied with the
transaction show significantly higher overall satisfaction (1.45) than any other group. In respect of
this group, a recovery paradox was not found and H1a is supported. However, if customers did not
report a failure and were just satisfied with the transaction, overall satisfaction (1.88) was significantly lower compared to the group who evaluated service recovery as “much better than expected”
(1.72). This supports H1b.
According to these results, service recovery efforts that exceed customer expectations can improve
customer overall satisfaction with the company, except for those customers who are very satisfied
with the transaction. However, if customers receive an error-free service that “just” satisfies their
needs, their overall satisfaction is lower compared to the group which was recovered successfully
after a service failure.
A slightly different pattern was found for “recommendation intention” (figure 2). Here, mean differences for group 1 and group 6 are not significant (z=-0.637, p>0.524). A very good service recovery
can lead to about the same recommendation intention (1.52) as indicated for the group which was
very satisfied with the transaction (1.46). H2a is supported. However, customers who assessed a
transaction as only “satisfying” showed a weaker recommendation intention (1.72) than customers
whose service recovery was much better than expected. Here, mean differences between group 1 and
7 are significant (z=-1.832, p<0.04) supporting H2b.
10
In summary, overall satisfaction and recommendation intention are highest for customers who receive an error-free and very satisfying service initially. However, customers who report a service
recovery episode that exceeds their expectations show a higher level of satisfaction and recommendation intention than customers who initially experienced an error-free and satisfying transaction.
When assessing the service encounter, customers were asked to report any deviation from expectation, not just “critical incidents” (Bitner et al. 1990). Next, the respondents assigned the failure into
one of three categories, “acceptable”, “not acceptable”, and “absolutely not acceptable”. Pretest
showed that “acceptable” failures did not call for service recovery very often. Hence, satisfaction
with recovery solution provided (distributive justice) and speed of recovery (procedural justice) was
only measured for “unacceptable” or “absolutely unacceptable” failures.
Both independent variables were measured on a five-point likert scale ranging from 1 (very satisfied)
to 5 (very dissatisfied). The dependent variable was measured on a five-point likert scale ranging
from 1 (much better than expected) to 5 (much worse than expected). Although likert scale measures
are ordinal, they are regularly treated as interval scales. Linear regression model was defined as
DC (SR) = f (SF (DJ), SF (PJ)),
where DC = disconfirmation (positive or negative); SR = service recovery; SF = satisfaction; DJ =
distributive justice; PJ = procedural justice.
Table 2
Linear regression model
Unstandardized
Standardized
T
coefficient
coefficient
B
Std. Error
Beta
Constant
2.050
.088
23.292
SF (PJ)
.347
.029
.526
11.939
SF (DJ)
.173
.036
.210
4.760
Dependent Variable: DC (SR), r=0.646, r2=0.418, corr. r2=0.415, Std. error of estimate=0.646
Sign.
.000
.000
.000
Since both coefficients are highly significant, H3a is supported. Our findings also support exploratory
research by (Parasuraman et al. 1991). They suggested that process dimension is more important
than outcome for service recovery.
In order to test the nonlinearity assumption (H3b), regression with optimal scaling (CATREG) is
applied. Standard linear regression maximizes the squared correlation between the dependent variable and a linear combination of metric independent variables. Ordinal variables are treated as interval level variables. Hence, only one parameter is estimated for each variable.
Regression with optimal scaling quantifies each level of an ordinal scale in order to maximize the
squared correlations. In other words, each ordinal level is scaled optimally to account for as much
variation in the transformed response as possible (SPSS 1998). The zero-order correlation measures
11
the association between the transformed predictors and the transformed response. The partial correlation removes the effects of other predictors from the predictor and the dependent variable. The part
correlation removes the effects of the other predictors from just the predictor. The importance is a
measure of each predictor’s contribution to the model. Procedural justice is almost three times more
important than distributive justice. The tolerance measures the proportion of the variability in each
predictor that is not accounted for by the other predictors (SPSS 1998).
Table 3
Regression with optimal scaling model
StandardF
ized
coefficient
Beta
Std. Error
Correlations
Zero-order
Importance Tolerance
Partial Part
SF (PJ)
.545
.041
179.786 .658
.570 .495
SF (DJ)
.268
.041
43.448 .498
.323 .243
Dependent Variable: DC (SR), multiple r=0.702, r2=0.498, corr. r2=0.489
.729
.271
After trans- Before
formation
transformation
.823
.805
.823
.805
Since corrected r2 has been increased compared to standard regression, nonlinearity assumption is
justified. While the quantification of the ordinal independent variables (satisfaction with outcome and
satisfaction with speed) remained almost linear, the quantification of the dependent variable (service
recovery disconfirmation) reveals nonlinearity.
Figure 3
Quantification of ordinal disconfirmation levels
Quantification of ordinal levels
Transformation diagram
Quantification of SR disconfirmation
2.5
2.0
1.5
1.0
.5
0.0
-.5
-1.0
-1.5
1 much better
2 better
SR disconfirmation
12
3 as expected
4 worse
5 much worse
Figure 3 shows that positive and neutral levels are close together, whereas negative levels are highly
dispersed. This implies that recovery outcome and speed have more impact on the negative than on
the positive side. Service organization cannot rely on generous and speedy service recovery merely in
order to delight dissatisfied customers. However, if the company fails on these dimensions, customers are very dissatisfied because of this “double deviation” (Bitner et al. 1990). In other words, to
solve the problems quickly is a basic requirement for service recoveries, but it is not enough to delight (Andreassen 2001) customers. Therefore, H3b is supported.
LIMITATION
While this study is one of the few that provides a test of the service recovery paradox using actual
satisfied and dissatisfied customers in real-life situations, some limitations remain. First, only the
banking industry was considered. Despite the fact that the banking industry is well suited because of
long-lasting, formal relationships, with many service transactions, generalization is not proved. Second, distributive justice and procedural justice are measured with only one item each. Scale reliability
and validity cannot be confirmed.
DISCUSSION
Prior research has not come to a conclusion regarding the existence of a service recovery paradox.
Some researchers suggest that customers prefer a reliable, error-free initial service over a very good
service recovery after a service failure, while others hypothesize the existence of a paradox. However, most studies fail to compare initially satisfied customers with customers who experienced a
service failure. By doing so, our results lead to the conclusion that a very satisfying initial service is
what is most preferred. Nevertheless, a very good service recovery leads to higher overall satisfaction and more positive word-of-mouth intention than an error-free initial service which is “just” satisfying.
Managerial implications are straightforward. Service managers have to strive for a reliable error-free
service in order to satisfy their customers in the first place. But, since service failures are inevitable in
most settings, service recovery becomes crucial when something goes wrong.
Our study further revealed that a fair solution and a speedy recovery are mandatory for successful
recovery. We found that distributive justice (e.g., satisfaction with the recovery solution) and procedural justice (e.g. satisfaction with speed) during the service recovery process explain almost 50 per
cent of the variance of disconfirmation with service recovery. Regression with optimal scaling confirms the predicted non-linearity between justice and service recovery disconfirmations. Unfair solutions and slow recovery may explain negative disconfirmation, while fair solutions and fast recovery
do not lead to positive disconfirmations. Hence, both factors are perceived as mandatory.
13
There are many avenues for further research. Firstly, it would be interesting to replicate a similar
study in other industries and in other countries. Secondly, justice constructs may be measured with
multi-item scales rather than single-item scales in real-life settings. Thirdly, antecedents of the service
recovery paradox should be taken into account when comparing initially satisfied and dissatisfied
customers. Finally, our study did not determine service recovery factors that “delight” customers.
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